StatLib---Andrews & Herzberg Archive This area contains files of data for the book DATA by Andrews and Herzberg. The following is a list of the files available. Each entry contains a description of the tables in the book and the first and last 4 lines of the data. Table 1.1 Sepal Length and Width and Petal Length and Width in Centimetres of Iris setosa, Iris versicolor and Iris virginica (3200 bytes) Table 2.1 Heights of Zea Mays (424 bytes) Table 3.1 Annual Number of Lynx Trappings in the MacKenzie River District for the Period 1821 to 1934 (1678 bytes) Table 3.2 Number of Lynx Pelts and Unit Price Paid to the Hudson's Bay Company for the Years 1857-1911 (1303 bytes) Table 4.1 Number of Deaths by Horsekicks in the Prussian Army from 1875-1894 for 14 Corps (1030 bytes) Table 5.1 Yearly Yields of Grain and Straw for Eighteen Plots in Broadbalk, Rothamsted 1852-1924 (18798 bytes) Table 6.1 The Plan of the Wheat Field and the Yield of Grain and Straw of Five Hundred Wheat Plots (6644 bytes)
Disinformation Visualization: How to lie with datavis | Visualising Information for Advocacy By Mushon Zer-Aviv, January 31, 2014 Seeing is believing. When working with raw data we’re often encouraged to present it differently, to give it a form, to map it or visualize it. But all maps lie. In fact, maps have to lie, otherwise they wouldn't be useful. Some are transparent and obvious lies, such as a tree icon on a map often represents more than one tree. It all sounds very sinister, and indeed sometimes it is. Over the past year I’ve had a few opportunities to run Disinformation Visualization workshops, encouraging activists, designers, statisticians, analysts, researchers, technologists and artists to visualize lies. Centuries before big data, computer graphics and social media collided and gave us the datavis explosion, visualization was mostly a scientific tool for inquiry and documentation. Reproducing Lies Let’s set up some rules. We don’t spread visual lies by presenting false data. Should we legalize the killing of babies? I would hope most of you would say: No.
about earth a visualization of global weather conditions forecast by supercomputers updated every three hours ocean surface current estimates updated every five days ocean surface temperatures and anomaly from daily average (1981-2011) updated daily ocean waves Aerosols and Chemistry | GEOS-5 (Goddard Earth Observing System) GMAO / NASA atmospheric pressure corresponds roughly to altitude several pressure layers are meteorologically interesting they show data assuming the earth is completely smooth note: 1 hectopascal (hPa) ≡ 1 millibar (mb) 1000 hPa | 00,~100 m, near sea level conditions 700 hPa | 0~3,500 m, planetary boundary, high 10 hPa | ~26,500 m, even more stratosphere the "Surface" layer represents conditions at ground or water level this layer follows the contours of mountains, valleys, etc. overlays show another dimension of data using color some overlays are valid at a specific height while others are valid for the entire thickness of the atmosphere Wind | wind speed at specified height Temp | Peak Wave Period |
Институт истории материальной культуры РАН — ИИМК РАН 2013 Kentucky Derby Props | Derby lines, futures wagers, betting tips, news and results Statistics for every Kentucky Derby since 1875, presented in 25 year intervals. Includes the names of the win, place and show horse, margin of victory, the winning jockey, total number of Derby nominees, number of starters, winning time, net pay-out to winner and track condition. A statistical graphics course and statistical graphics advice Dean Eckles writes: Some of my coworkers at Facebook and I have worked with Udacity to create an online course on exploratory data analysis, including using data visualizations in R as part of EDA.The course has now launched at so anyone can take it for free. And Kaiser Fung has reviewed it. So definitely feel free to promote it! I didn’t have a chance to look at the course so instead I responded with some generic comments about eda and visualization (in no particular order): - Think of a graph as a comparison. - For example, Tukey described EDA as the search for the unexpected (or something like that, I don’t remember the exact quote). - Consider two extreme views: (a) a graph as a pure exploration, where you bring no expectations whatsoever to the data, (b) a graph as pure execution, you know what you want to show and then you show it. - No need to cram all information onto a single graph. - I like line plots.